Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item A more generalizable DNN based Automatic Segmentation of Brain Tumors from Multimodal low-resolution 2D MRI(Institute of Electrical and Electronics Engineers Inc., 2021) Bhaskaracharya, B.; Nair, R.P.; Prakashini, K.; Girish Menon, R.; Litvak, P.; Mandava, P.; Vijayasenan, D.; Sumam David, S.In the field of Neuro-oncology, there is a need for improved diagnosis and prognosis of brain tumors. Brain tumor segmentation is important for treatment planning and assessing the treatment outcomes. Manual segmentation of brain tumors is tedious, time-consuming, and subjective. In this work, an efficient encoder-decoder based architectures were implemented for automatic segmentation of brain tumors from low resolution 2D images. Ensemble of the multiple architectures (EMMA) improves the performance of the brain tumor segmentation. Furthermore, the computational requirements of the proposed models are lower than that of BraTS-challenge methods. The average Fl-scores on the BraTS-challenge validation dataset for Tumor Core, Whole Tumor, and Enhancing Tumor are 0.82, 0.87, and 0.78, respectively. The average Fl-scores on the KMC-Manipal dataset for TC, WT, and ET are 0.74, 0.82, and 0.68 respectively. © 2021 IEEE.Item Computational Studies on the Hemodynamics of Patient-Specific Human Carotid Artery(Springer Science and Business Media Deutschland GmbH, 2023) Rakesh, L.; Anees Fahim, C.P.; Prakashini, K.; Anish, S.Atherosclerosis is a cardiovascular disease that affects large and medium-sized arteries and is characterised by intricate interactions between the artery wall and pulsatile blood flow. The current research focuses on the hemodynamics of the human carotid artery in both healthy and stenosed patients. Using the 3D Slicer, CT images of patients are rebuilt to get the three-dimensional geometry of the carotid artery. To further understand the effects of hemodynamic factors, computational experiments are conducted. The study used Time-Averaged Wall Shear Stress (TAWSS), OSI (Oscillating Shear Index), and RRT (Relative Residence Time) as hemodynamic parameters to characterise the flow behaviour. In this study, we have undertaken CFD studies on hemodynamic descriptors of a healthy normal artery (Case A) and unhealthy stenosed artery (Case B). The study concludes that there is a significant variation in the hemodynamic descriptors taken for study in the case of an unhealthy stenosed artery. High values of OSI and RRT are noticed in the case of an unhealthy stenosed artery. The larger magnitudes in the hemodynamic parameters indicate associated risk factors to progress and thus promotes atherosclerosis. All of these are effective in determining the loss of vascular function and the vessel tissue's integrity. For clinical diagnosis and further anatomical evidence, the indicated hemodynamics examination platform is relatively effective for clinicians. The novelty of this work is that we have used patient specific carotid artery of healthy and unhealthy artery, reconstructed artery from CT scans using appropriate medical imaging softwares, used physiological pulsatile flow for velocity input, coded an user defined function for the hemodynamic parameters like TAWSS, OSI, and RRT. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Effect of Induced Helicity on the Hemodynamics of Carotid Artery Passage(Springer Science and Business Media Deutschland GmbH, 2024) Rakesh, L.; Kadali, A.; Prakashini, K.; Anish, S.Abrupt narrowing of the carotid artery known as atherosclerosis is a common cardiovascular disease, increasing the risk of stroke which is one of the leading causes of death. Helicity in the arterial passage is found to be one of the effective ways to minimize plaque formation. Using Autodesk Meshmixer, an open-source software, the stenosed portion of the diseased artery is removed to obtain what is referred to in this study as the base case. The helicity and hemodynamic characteristics of a patient-specific geometry with and without stent in repaired instance are examined. The current study found that when novel stent design is placed there is a reduction in recirculation zone size and Relative Residence Time (RRT), but also resulted in increased pressure drop across the artery. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item A hybrid CNN-FC approach for automatic grading of brain tumors from non-invasive MRIs(Institute of Electrical and Electronics Engineers Inc., 2024) Bhaskaracharya, B.; Nair, R.P.; Prakashini, K.; Girish Menon, R.; Litvak, P.; Mandava, P.; Vijayasenan, D.; Sumam David, S.The grading of brain tumors is essential in treatment planning to effectively control the tumor growth and reduce the associated symptoms. Appropriate treatment planning might help in improving the quality of life and patient life span. Gliomas are indeed the most common type of brain tumor, originating from glial cells. Low-grade gliomas (grades 1 or 2) are typically slow-growing, less invasive, and may be suitable for surgical resection or targeted therapies. On the other hand, higher-grade tumors such as grades 3 or 4 are more aggressive, it might infiltrate the surrounding brain tissue making complete resection challenging. In clinical diagnosis, traditionally tumor grading requires the procedure of resecting a part of the tumor for microscopic examination. To address this, a method to grade the tumor non-invasively using MRIs is proposed. Our work utilized the BraTS2018 dataset to segment the substructure of brain tumors that includes necrosis and non-enhancing, edema, and enhancing regions. These regions are then used to train the proposed grading model. Furthermore, we evaluated the performance of our model on a tertiary hospital dataset consisting of 69 samples. The accuracy scores obtained on the BraTS2018 test sample and tertiary hospital dataset are 0.87 and, 0.85 respectively. This consistent score on both public and tertiary hospital datasets indicates a reliable and stable performance of the model. © 2024 IEEE.